if (!require("remotes"))
install.packages("remotes")
remotes::install_github("flavjack/inti")ESTIMULANTES EN LA GERMINACIÓN Y BIOMETRÍA INICIAL DE DOS VARIEDADES DE MAÍZ MORADO (Zea mays L.)
1 Setup
Instalar version en desarrollo.
library(emmeans)
library(corrplot)
library(multcomp)
library(factoextra)
library(corrplot)
library(png)
source('https://inkaverse.com/setup.r')
cat("Project: ", getwd())Project: C:/INIA/GIT/prochira_maiz_morado
session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.1 (2024-06-14 ucrt)
os Windows 11 x64 (build 22631)
system x86_64, mingw32
ui RTerm
language (EN)
collate Spanish_Peru.utf8
ctype Spanish_Peru.utf8
tz America/Lima
date 2024-08-19
pandoc 3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
agricolae 1.3-7 2023-10-22 [1] CRAN (R 4.4.0)
AlgDesign 1.2.1 2022-05-25 [1] CRAN (R 4.4.0)
askpass 1.2.0 2023-09-03 [1] CRAN (R 4.4.0)
boot 1.3-30 2024-02-26 [2] CRAN (R 4.4.1)
cachem 1.1.0 2024-05-16 [1] CRAN (R 4.4.0)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.4.0)
cli 3.6.2 2023-12-11 [1] CRAN (R 4.4.0)
cluster 2.1.6 2023-12-01 [2] CRAN (R 4.4.1)
coda 0.19-4.1 2024-01-31 [1] CRAN (R 4.4.0)
codetools 0.2-20 2024-03-31 [2] CRAN (R 4.4.1)
colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.4.0)
corrplot * 0.92 2021-11-18 [1] CRAN (R 4.4.0)
cowplot * 1.1.3 2024-01-22 [1] CRAN (R 4.4.0)
curl 5.2.1 2024-03-01 [1] CRAN (R 4.4.0)
devtools * 2.4.5 2022-10-11 [1] CRAN (R 4.4.0)
digest 0.6.35 2024-03-11 [1] CRAN (R 4.4.0)
dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.4.0)
DT 0.33 2024-04-04 [1] CRAN (R 4.4.0)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.4.0)
emmeans * 1.10.2 2024-05-20 [1] CRAN (R 4.4.0)
estimability 1.5.1 2024-05-12 [1] CRAN (R 4.4.0)
evaluate 0.23 2023-11-01 [1] CRAN (R 4.4.0)
factoextra * 1.0.7 2020-04-01 [1] CRAN (R 4.4.0)
FactoMineR * 2.11 2024-04-20 [1] CRAN (R 4.4.0)
fansi 1.0.6 2023-12-08 [1] CRAN (R 4.4.0)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
flashClust 1.01-2 2012-08-21 [1] CRAN (R 4.4.0)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.4.0)
fs 1.6.4 2024-04-25 [1] CRAN (R 4.4.0)
gargle 1.5.2 2023-07-20 [1] CRAN (R 4.4.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.4.0)
ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.4.0)
ggrepel 0.9.5 2024-01-10 [1] CRAN (R 4.4.0)
glue 1.7.0 2024-01-09 [1] CRAN (R 4.4.0)
googledrive * 2.1.1 2023-06-11 [1] CRAN (R 4.4.0)
googlesheets4 * 1.1.1 2023-06-11 [1] CRAN (R 4.4.0)
gsheet * 0.4.5 2020-04-07 [1] CRAN (R 4.4.0)
gtable 0.3.5 2024-04-22 [1] CRAN (R 4.4.0)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.0)
htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.4.0)
httpuv 1.6.15 2024-03-26 [1] CRAN (R 4.4.0)
httr 1.4.7 2023-08-15 [1] CRAN (R 4.4.0)
huito * 0.2.4 2023-10-25 [1] CRAN (R 4.4.0)
inti * 0.6.5 2024-08-02 [1] Github (flavjack/inti@38be898)
jsonlite 1.8.8 2023-12-04 [1] CRAN (R 4.4.0)
knitr * 1.46 2024-04-06 [1] CRAN (R 4.4.0)
later 1.3.2 2023-12-06 [1] CRAN (R 4.4.0)
lattice 0.22-6 2024-03-20 [2] CRAN (R 4.4.1)
leaps 3.1 2020-01-16 [1] CRAN (R 4.4.0)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0)
lme4 1.1-35.3 2024-04-16 [1] CRAN (R 4.4.0)
lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.4.0)
magick * 2.8.3 2024-02-18 [1] CRAN (R 4.4.0)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0)
MASS * 7.3-60.2 2024-04-26 [2] CRAN (R 4.4.1)
Matrix 1.7-0 2024-04-26 [2] CRAN (R 4.4.1)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.4.0)
mime 0.12 2021-09-28 [1] CRAN (R 4.4.0)
miniUI 0.1.1.1 2018-05-18 [1] CRAN (R 4.4.0)
minqa 1.2.7 2024-05-20 [1] CRAN (R 4.4.0)
mnormt 2.1.1 2022-09-26 [1] CRAN (R 4.4.0)
multcomp * 1.4-25 2023-06-20 [1] CRAN (R 4.4.0)
multcompView 0.1-10 2024-03-08 [1] CRAN (R 4.4.0)
munsell 0.5.1 2024-04-01 [1] CRAN (R 4.4.0)
mvtnorm * 1.2-5 2024-05-21 [1] CRAN (R 4.4.0)
nlme 3.1-164 2023-11-27 [2] CRAN (R 4.4.1)
nloptr 2.0.3 2022-05-26 [1] CRAN (R 4.4.0)
openssl 2.2.0 2024-05-16 [1] CRAN (R 4.4.0)
pillar 1.9.0 2023-03-22 [1] CRAN (R 4.4.0)
pkgbuild 1.4.4 2024-03-17 [1] CRAN (R 4.4.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0)
pkgload 1.3.4 2024-01-16 [1] CRAN (R 4.4.0)
png * 0.1-8 2022-11-29 [1] CRAN (R 4.4.0)
profvis 0.3.8 2023-05-02 [1] CRAN (R 4.4.0)
promises 1.3.0 2024-04-05 [1] CRAN (R 4.4.0)
psych * 2.4.3 2024-03-18 [1] CRAN (R 4.4.0)
purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.4.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.0)
rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.4.0)
Rcpp 1.0.12 2024-01-09 [1] CRAN (R 4.4.0)
readr * 2.1.5 2024-01-10 [1] CRAN (R 4.4.0)
remotes 2.5.0 2024-03-17 [1] CRAN (R 4.4.0)
rlang 1.1.3 2024-01-10 [1] CRAN (R 4.4.0)
rmarkdown 2.27 2024-05-17 [1] CRAN (R 4.4.0)
rstudioapi 0.16.0 2024-03-24 [1] CRAN (R 4.4.0)
sandwich 3.1-0 2023-12-11 [1] CRAN (R 4.4.0)
scales 1.3.0 2023-11-28 [1] CRAN (R 4.4.0)
scatterplot3d 0.3-44 2023-05-05 [1] CRAN (R 4.4.0)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.0)
shiny * 1.8.1.1 2024-04-02 [1] CRAN (R 4.4.0)
showtext 0.9-7 2024-03-02 [1] CRAN (R 4.4.0)
showtextdb 3.0 2020-06-04 [1] CRAN (R 4.4.0)
stringi 1.8.4 2024-05-06 [1] CRAN (R 4.4.0)
stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.4.0)
survival * 3.6-4 2024-04-24 [2] CRAN (R 4.4.1)
sysfonts 0.8.9 2024-03-02 [1] CRAN (R 4.4.0)
TH.data * 1.1-2 2023-04-17 [1] CRAN (R 4.4.0)
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.4.0)
tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.4.0)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.4.0)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.4.0)
timechange 0.3.0 2024-01-18 [1] CRAN (R 4.4.0)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.4.0)
urlchecker 1.0.1 2021-11-30 [1] CRAN (R 4.4.0)
usethis * 2.2.3 2024-02-19 [1] CRAN (R 4.4.0)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.4.0)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0)
withr 3.0.0 2024-01-16 [1] CRAN (R 4.4.0)
xfun 0.44 2024-05-15 [1] CRAN (R 4.4.0)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.4.0)
yaml 2.3.8 2023-12-11 [1] CRAN (R 4.4.0)
zoo 1.8-12 2023-04-13 [1] CRAN (R 4.4.0)
[1] C:/Users/INIA/AppData/Local/R/win-library/4.4
[2] C:/Program Files/R/R-4.4.1/library
──────────────────────────────────────────────────────────────────────────────
2 Refrencias
- (PCA) https://www.r-bloggers.com/2017/07/pca-course-using-factominer/
- (PCA) https://www.youtube.com/watch?v=Uhw-1NilmAk&ab_channel=Fran%C3%A7oisHusson
- (HCPC) https://youtu.be/EJqYTDTJJug
3 Import data
https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741
url <- "https://docs.google.com/spreadsheets/d/1E_l9uV3MT1qlJuVtWK66NgevqPH6fVJCekqNhS_VGm0/edit?gid=1893553741#gid=1893553741"
gs <- url %>%
as_sheets_id()
imbibition <- gs %>%
range_read("imbibition") %>%
rename_with(~ tolower(.)) %>%
mutate(time = tiempo, .after = tiempo) %>%
mutate(variedad = case_when(
variedad %in% c("criollo") ~ "Creole"
, variedad %in% c("Hibrido") ~ "Hybrid"
)) %>%
mutate(across(1:tiempo, ~ as.factor(.)))
str(imbibition)
## tibble [2,100 × 7] (S3: tbl_df/tbl/data.frame)
## $ bloque : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ trat : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ variedad : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
## $ tiempo : Factor w/ 5 levels "0","3","6","9",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ time : num [1:2100] 0 0 0 0 0 0 0 0 0 0 ...
## $ peso : num [1:2100] 0.58 0.62 0.73 0.72 0.72 0.68 0.71 0.61 0.69 0.64 ...
germination <- gs %>%
range_read("germination") %>%
rename_with(~ tolower(.)) %>%
mutate(variedad = case_when(
variedad %in% c("criollo") ~ "Creole"
, variedad %in% c("Hibrido") ~ "Hybrid"
)) %>%
mutate(trat = case_when(
tratamiento %in% "Agua Destilada" ~ "T0"
, tratamiento %in% "Algas Marinas 1 L/cil" ~ "T1"
, tratamiento %in% "Algas Marinas 1,5 L/cil" ~ "T2"
, tratamiento %in% "Azufre 100 gr.200 L-1" ~ "T3"
, tratamiento %in% "Azufre 150 gr.200 L-1" ~ "T4"
, tratamiento %in% "Suero de leche 10%" ~ "T5"
, tratamiento %in% "Suero de leche 30%" ~ "T6"
), .before = tratamiento) %>%
mutate(across(1:variedad, ~ as.factor(.)))
str(germination)
## tibble [42 × 11] (S3: tbl_df/tbl/data.frame)
## $ bloque : Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3 1 ...
## $ trat : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 2 2 2 3 3 3 4 ...
## $ tratamiento: Factor w/ 7 levels "Agua Destilada",..: 1 1 1 2 2 2 3 3 3 4 ...
## $ variedad : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
## $ dia 1 : num [1:42] 2 4 3 0 1 1 0 0 1 0 ...
## $ dia 2 : num [1:42] 5 4 5 3 2 1 1 4 5 1 ...
## $ dia 3 : num [1:42] 1 1 1 1 0 0 0 0 0 0 ...
## $ total : num [1:42] 8 9 9 4 3 2 1 4 6 1 ...
## $ pg : num [1:42] 80 90 90 40 30 20 10 40 60 10 ...
## $ vg : num [1:42] 2.67 3 3 2 1.5 ...
## $ ig : num [1:42] 2.4 2.7 2.7 0.8 0.6 0.4 0.1 0.4 1.2 0.1 ...
plantula <- gs %>%
range_read("plantula") %>%
rename_with(~ tolower(.)) %>%
mutate(variedad = case_when(
variedad %in% c("criollo") ~ "Creole"
, variedad %in% c("hibrido") ~ "Hybrid"
)) %>%
mutate(across(1:variedad, ~ as.factor(.)))
str(plantula)
## tibble [210 × 17] (S3: tbl_df/tbl/data.frame)
## $ bloque : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 2 2 2 2 2 ...
## $ trat : Factor w/ 7 levels "T0","T1","T2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ tratamiento : Factor w/ 7 levels "Agua Destilada",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ variedad : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 1 1 1 ...
## $ raiz_lgtd : num [1:210] 11 8 12 11 8 13 10 12 9 13 ...
## $ gsr_raiz : num [1:210] 1.3 1.19 1.51 1.21 1.17 1.13 1.68 1.27 1.03 1.16 ...
## $ num_raiz : num [1:210] 8 11 11 9 12 16 10 9 16 11 ...
## $ peso_fres_raiz : num [1:210] 4.82 3.21 4.91 4.42 4.62 6.07 4.97 6.13 3.05 4 ...
## $ peso_seco_raiz : num [1:210] 0.73 0.41 0.62 0.66 0.72 0.54 0.75 0.56 0.57 0.74 ...
## $ alt_planta : num [1:210] 30 26 28 32 25 27 28 35 29 29 ...
## $ gsr_tallo : num [1:210] 5.86 4.56 6.59 4.63 4.55 4.14 4.02 4.32 3.45 3.61 ...
## $ nhp_hoja : num [1:210] 5 5 5 6 4 5 5 5 5 5 ...
## $ larg_hoja : num [1:210] 26 23 21 27 29 22 24 30 25 23 ...
## $ grs_hoja : num [1:210] 0.94 1.15 0.89 0.98 1.01 0.72 0.62 1.03 0.71 1.34 ...
## $ anch_hoja : num [1:210] 19.3 19.9 21.5 17.3 18.9 ...
## $ peso_fres_brote: num [1:210] 5.34 5.99 5.45 4.81 7.03 6.79 4.99 4.53 3.56 4 ...
## $ peso_seco_brote: num [1:210] 0.5 0.49 1.04 0.78 0.68 0.67 0.69 0.78 0.73 0.75 ...4 Tratamientos
imbibition %>%
group_by(trat, tratamiento) %>%
summarise(n = n()) %>%
select(!n)
## # A tibble: 7 × 2
## # Groups: trat [7]
## trat tratamiento
## <fct> <fct>
## 1 T0 Agua Destilada
## 2 T1 Algas Marinas 1 L/cil
## 3 T2 Algas Marinas 1,5 L/cil
## 4 T3 Azufre 100 gr.200 L-1
## 5 T4 Azufre 150 gr.200 L-1
## 6 T5 Suero de leche 10%
## 7 T6 Suero de leche 30%5 Data summary
sm <- imbibition %>%
group_by(tratamiento, variedad, tiempo) %>%
summarise(across(peso, ~ sum(!is.na(.))))
sm
## # A tibble: 70 × 4
## # Groups: tratamiento, variedad [14]
## tratamiento variedad tiempo peso
## <fct> <fct> <fct> <int>
## 1 Agua Destilada Creole 0 30
## 2 Agua Destilada Creole 3 30
## 3 Agua Destilada Creole 6 30
## 4 Agua Destilada Creole 9 30
## 5 Agua Destilada Creole 12 30
## 6 Agua Destilada Hybrid 0 30
## 7 Agua Destilada Hybrid 3 30
## 8 Agua Destilada Hybrid 6 30
## 9 Agua Destilada Hybrid 9 30
## 10 Agua Destilada Hybrid 12 30
## # ℹ 60 more rows
sm <- germination %>%
group_by(tratamiento, variedad) %>%
summarise(across(pg:ig, ~ sum(!is.na(.))))
sm
## # A tibble: 14 × 5
## # Groups: tratamiento [7]
## tratamiento variedad pg vg ig
## <fct> <fct> <int> <int> <int>
## 1 Agua Destilada Creole 3 3 3
## 2 Agua Destilada Hybrid 3 3 3
## 3 Algas Marinas 1 L/cil Creole 3 3 3
## 4 Algas Marinas 1 L/cil Hybrid 3 3 3
## 5 Algas Marinas 1,5 L/cil Creole 3 3 3
## 6 Algas Marinas 1,5 L/cil Hybrid 3 3 3
## 7 Azufre 100 gr.200 L-1 Creole 3 3 3
## 8 Azufre 100 gr.200 L-1 Hybrid 3 3 3
## 9 Azufre 150 gr.200 L-1 Creole 3 3 3
## 10 Azufre 150 gr.200 L-1 Hybrid 3 3 3
## 11 Suero de leche 10% Creole 3 3 3
## 12 Suero de leche 10% Hybrid 3 3 3
## 13 Suero de leche 30% Creole 3 3 3
## 14 Suero de leche 30% Hybrid 3 3 3
sm <- plantula %>%
group_by(tratamiento, variedad) %>%
summarise(across(where(is.numeric), ~ sum(!is.na(.))))
sm
## # A tibble: 14 × 15
## # Groups: tratamiento [7]
## tratamiento variedad raiz_lgtd gsr_raiz num_raiz peso_fres_raiz
## <fct> <fct> <int> <int> <int> <int>
## 1 Agua Destilada Creole 15 15 15 15
## 2 Agua Destilada Hybrid 15 15 15 15
## 3 Algas Marinas 1 L/cil Creole 15 15 15 15
## 4 Algas Marinas 1 L/cil Hybrid 15 15 15 15
## 5 Algas Marinas 1,5 L/cil Creole 15 15 15 15
## 6 Algas Marinas 1,5 L/cil Hybrid 15 15 15 15
## 7 Azufre 100 gr.200 L-1 Creole 15 15 15 15
## 8 Azufre 100 gr.200 L-1 Hybrid 15 15 15 15
## 9 Azufre 150 gr.200 L-1 Creole 15 15 15 15
## 10 Azufre 150 gr.200 L-1 Hybrid 15 15 15 15
## 11 Suero de leche 10% Creole 15 15 15 15
## 12 Suero de leche 10% Hybrid 15 15 15 15
## 13 Suero de leche 30% Creole 15 15 15 15
## 14 Suero de leche 30% Hybrid 15 15 15 15
## # ℹ 9 more variables: peso_seco_raiz <int>, alt_planta <int>, gsr_tallo <int>,
## # nhp_hoja <int>, larg_hoja <int>, grs_hoja <int>, anch_hoja <int>,
## # peso_fres_brote <int>, peso_seco_brote <int>6 Objetivos
Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.
Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.
6.1 Objetivo Específico 1
Evaluar los parámetros de germinación de dos variedades de semillas de maiz morado usando bioestimulante orgánico.
- Imbibiciación, % germinación, velocidad e IG
6.1.1 Imbibición
trait <- "peso"
fb <- imbibition
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad + (1 + tiempo|tratamiento)") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + tiempo + trat*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers %>% kable()| index | bloque | trat | variedad | tiempo | tratamiento | peso | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag |
|---|
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: peso
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 0.0021 0.00105 0.1222 0.885
## tiempo 4 10.0058 2.50146 289.7715 <0.0000000000000002 ***
## trat 6 3.2174 0.53624 62.1186 <0.0000000000000002 ***
## variedad 1 0.6165 0.61649 71.4150 <0.0000000000000002 ***
## trat:variedad 6 2.6467 0.44111 51.0987 <0.0000000000000002 ***
## Residuals 2080 17.9556 0.00863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ tiempo|variedad|trat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| tiempo | variedad | trat | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | 12 | Creole | T0 | 0.8296986 | 0.0086019 | 2080 | 0.8128293 | 0.8465678 | a |
| 3 | 9 | Creole | T0 | 0.8279129 | 0.0086019 | 2080 | 0.8110436 | 0.8447821 | a |
| 2 | 3 | Creole | T0 | 0.7645081 | 0.0086019 | 2080 | 0.7476388 | 0.7813774 | b |
| 4 | 6 | Creole | T0 | 0.7620295 | 0.0086019 | 2080 | 0.7451603 | 0.7788988 | b |
| 5 | 0 | Creole | T0 | 0.6398176 | 0.0086019 | 2080 | 0.6229483 | 0.6566869 | c |
| 11 | 12 | Creole | T1 | 0.7157719 | 0.0086019 | 2080 | 0.6989026 | 0.7326412 | a |
| 13 | 9 | Creole | T1 | 0.7139862 | 0.0086019 | 2080 | 0.6971169 | 0.7308555 | a |
| 12 | 3 | Creole | T1 | 0.6505814 | 0.0086019 | 2080 | 0.6337122 | 0.6674507 | b |
| 14 | 6 | Creole | T1 | 0.6481029 | 0.0086019 | 2080 | 0.6312336 | 0.6649721 | b |
| 15 | 0 | Creole | T1 | 0.5258910 | 0.0086019 | 2080 | 0.5090217 | 0.5427602 | c |
| 21 | 12 | Creole | T2 | 0.6749719 | 0.0086019 | 2080 | 0.6581026 | 0.6918412 | a |
| 23 | 9 | Creole | T2 | 0.6731862 | 0.0086019 | 2080 | 0.6563169 | 0.6900555 | a |
| 22 | 3 | Creole | T2 | 0.6097814 | 0.0086019 | 2080 | 0.5929122 | 0.6266507 | b |
| 24 | 6 | Creole | T2 | 0.6073029 | 0.0086019 | 2080 | 0.5904336 | 0.6241721 | b |
| 25 | 0 | Creole | T2 | 0.4850910 | 0.0086019 | 2080 | 0.4682217 | 0.5019602 | c |
| 31 | 12 | Creole | T3 | 0.6591052 | 0.0086019 | 2080 | 0.6422360 | 0.6759745 | a |
| 33 | 9 | Creole | T3 | 0.6573195 | 0.0086019 | 2080 | 0.6404503 | 0.6741888 | a |
| 32 | 3 | Creole | T3 | 0.5939148 | 0.0086019 | 2080 | 0.5770455 | 0.6107840 | b |
| 34 | 6 | Creole | T3 | 0.5914362 | 0.0086019 | 2080 | 0.5745669 | 0.6083055 | b |
| 35 | 0 | Creole | T3 | 0.4692243 | 0.0086019 | 2080 | 0.4523550 | 0.4860936 | c |
| 41 | 12 | Creole | T4 | 0.6322386 | 0.0086019 | 2080 | 0.6153693 | 0.6491078 | a |
| 43 | 9 | Creole | T4 | 0.6304529 | 0.0086019 | 2080 | 0.6135836 | 0.6473221 | a |
| 42 | 3 | Creole | T4 | 0.5670481 | 0.0086019 | 2080 | 0.5501788 | 0.5839174 | b |
| 44 | 6 | Creole | T4 | 0.5645695 | 0.0086019 | 2080 | 0.5477003 | 0.5814388 | b |
| 45 | 0 | Creole | T4 | 0.4423576 | 0.0086019 | 2080 | 0.4254883 | 0.4592269 | c |
| 51 | 12 | Creole | T5 | 0.8092386 | 0.0086019 | 2080 | 0.7923693 | 0.8261078 | a |
| 53 | 9 | Creole | T5 | 0.8074529 | 0.0086019 | 2080 | 0.7905836 | 0.8243221 | a |
| 52 | 3 | Creole | T5 | 0.7440481 | 0.0086019 | 2080 | 0.7271788 | 0.7609174 | b |
| 54 | 6 | Creole | T5 | 0.7415695 | 0.0086019 | 2080 | 0.7247003 | 0.7584388 | b |
| 55 | 0 | Creole | T5 | 0.6193576 | 0.0086019 | 2080 | 0.6024883 | 0.6362269 | c |
| 61 | 12 | Creole | T6 | 0.7740386 | 0.0086019 | 2080 | 0.7571693 | 0.7909078 | a |
| 63 | 9 | Creole | T6 | 0.7722529 | 0.0086019 | 2080 | 0.7553836 | 0.7891221 | a |
| 62 | 3 | Creole | T6 | 0.7088481 | 0.0086019 | 2080 | 0.6919788 | 0.7257174 | b |
| 64 | 6 | Creole | T6 | 0.7063695 | 0.0086019 | 2080 | 0.6895003 | 0.7232388 | b |
| 65 | 0 | Creole | T6 | 0.5841576 | 0.0086019 | 2080 | 0.5672883 | 0.6010269 | c |
| 6 | 12 | Hybrid | T0 | 0.7764386 | 0.0086019 | 2080 | 0.7595693 | 0.7933078 | a |
| 8 | 9 | Hybrid | T0 | 0.7746529 | 0.0086019 | 2080 | 0.7577836 | 0.7915221 | a |
| 7 | 3 | Hybrid | T0 | 0.7112481 | 0.0086019 | 2080 | 0.6943788 | 0.7281174 | b |
| 9 | 6 | Hybrid | T0 | 0.7087695 | 0.0086019 | 2080 | 0.6919003 | 0.7256388 | b |
| 10 | 0 | Hybrid | T0 | 0.5865576 | 0.0086019 | 2080 | 0.5696883 | 0.6034269 | c |
| 16 | 12 | Hybrid | T1 | 0.7279719 | 0.0086019 | 2080 | 0.7111026 | 0.7448412 | a |
| 18 | 9 | Hybrid | T1 | 0.7261862 | 0.0086019 | 2080 | 0.7093169 | 0.7430555 | a |
| 17 | 3 | Hybrid | T1 | 0.6627814 | 0.0086019 | 2080 | 0.6459122 | 0.6796507 | b |
| 19 | 6 | Hybrid | T1 | 0.6603029 | 0.0086019 | 2080 | 0.6434336 | 0.6771721 | b |
| 20 | 0 | Hybrid | T1 | 0.5380910 | 0.0086019 | 2080 | 0.5212217 | 0.5549602 | c |
| 26 | 12 | Hybrid | T2 | 0.7881052 | 0.0086019 | 2080 | 0.7712360 | 0.8049745 | a |
| 28 | 9 | Hybrid | T2 | 0.7863195 | 0.0086019 | 2080 | 0.7694503 | 0.8031888 | a |
| 27 | 3 | Hybrid | T2 | 0.7229148 | 0.0086019 | 2080 | 0.7060455 | 0.7397840 | b |
| 29 | 6 | Hybrid | T2 | 0.7204362 | 0.0086019 | 2080 | 0.7035669 | 0.7373055 | b |
| 30 | 0 | Hybrid | T2 | 0.5982243 | 0.0086019 | 2080 | 0.5813550 | 0.6150936 | c |
| 36 | 12 | Hybrid | T3 | 0.7332386 | 0.0086019 | 2080 | 0.7163693 | 0.7501078 | a |
| 38 | 9 | Hybrid | T3 | 0.7314529 | 0.0086019 | 2080 | 0.7145836 | 0.7483221 | a |
| 37 | 3 | Hybrid | T3 | 0.6680481 | 0.0086019 | 2080 | 0.6511788 | 0.6849174 | b |
| 39 | 6 | Hybrid | T3 | 0.6655695 | 0.0086019 | 2080 | 0.6487003 | 0.6824388 | b |
| 40 | 0 | Hybrid | T3 | 0.5433576 | 0.0086019 | 2080 | 0.5264883 | 0.5602269 | c |
| 46 | 12 | Hybrid | T4 | 0.7735052 | 0.0086019 | 2080 | 0.7566360 | 0.7903745 | a |
| 48 | 9 | Hybrid | T4 | 0.7717195 | 0.0086019 | 2080 | 0.7548503 | 0.7885888 | a |
| 47 | 3 | Hybrid | T4 | 0.7083148 | 0.0086019 | 2080 | 0.6914455 | 0.7251840 | b |
| 49 | 6 | Hybrid | T4 | 0.7058362 | 0.0086019 | 2080 | 0.6889669 | 0.7227055 | b |
| 50 | 0 | Hybrid | T4 | 0.5836243 | 0.0086019 | 2080 | 0.5667550 | 0.6004936 | c |
| 56 | 12 | Hybrid | T5 | 0.7615719 | 0.0086019 | 2080 | 0.7447026 | 0.7784412 | a |
| 58 | 9 | Hybrid | T5 | 0.7597862 | 0.0086019 | 2080 | 0.7429169 | 0.7766555 | a |
| 57 | 3 | Hybrid | T5 | 0.6963814 | 0.0086019 | 2080 | 0.6795122 | 0.7132507 | b |
| 59 | 6 | Hybrid | T5 | 0.6939029 | 0.0086019 | 2080 | 0.6770336 | 0.7107721 | b |
| 60 | 0 | Hybrid | T5 | 0.5716910 | 0.0086019 | 2080 | 0.5548217 | 0.5885602 | c |
| 66 | 12 | Hybrid | T6 | 0.7741052 | 0.0086019 | 2080 | 0.7572360 | 0.7909745 | a |
| 68 | 9 | Hybrid | T6 | 0.7723195 | 0.0086019 | 2080 | 0.7554503 | 0.7891888 | a |
| 67 | 3 | Hybrid | T6 | 0.7089148 | 0.0086019 | 2080 | 0.6920455 | 0.7257840 | b |
| 69 | 6 | Hybrid | T6 | 0.7064362 | 0.0086019 | 2080 | 0.6895669 | 0.7233055 | b |
| 70 | 0 | Hybrid | T6 | 0.5842243 | 0.0086019 | 2080 | 0.5673550 | 0.6010936 | c |
p1a <- mc %>%
plot_smr(type = "line"
, x = "tiempo"
, y = "emmean"
, group = "trat"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Seed weight (g)"
, xlab = "Time (h)"
, glab = "Treatment"
, ylimits = c(0.4, 1, 0.2)
) +
facet_wrap(. ~ variedad, ncol = 2) +
theme(legend.position = "top") +
guides(colour = guide_legend(nrow = 1))
p1a6.1.2 Porcentaje de Germination
trait <- "pg"
fb <- germination
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers %>% kable()| index | bloque | trat | variedad | pg | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag |
|---|
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: pg
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 633.3 316.7 0.6222 0.544582
## trat 6 7000.0 1166.7 2.2922 0.065673 .
## variedad 1 4609.5 4609.5 9.0565 0.005753 **
## trat:variedad 6 6857.1 1142.9 2.2454 0.070466 .
## Residuals 26 13233.3 509.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ variedad|trat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| variedad | trat | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | Creole | T0 | 86.66667 | 13.02529 | 26 | 59.8928044 | 113.44053 | a |
| 1 | Hybrid | T0 | 63.33333 | 13.02529 | 26 | 36.5594710 | 90.10720 | a |
| 3 | Hybrid | T1 | 70.00000 | 13.02529 | 26 | 43.2261377 | 96.77386 | a |
| 4 | Creole | T1 | 30.00000 | 13.02529 | 26 | 3.2261377 | 56.77386 | b |
| 5 | Hybrid | T2 | 56.66667 | 13.02529 | 26 | 29.8928044 | 83.44053 | a |
| 6 | Creole | T2 | 36.66667 | 13.02529 | 26 | 9.8928044 | 63.44053 | a |
| 7 | Hybrid | T3 | 66.66667 | 13.02529 | 26 | 39.8928044 | 93.44053 | a |
| 8 | Creole | T3 | 16.66667 | 13.02529 | 26 | -10.1071956 | 43.44053 | b |
| 9 | Hybrid | T4 | 76.66667 | 13.02529 | 26 | 49.8928044 | 103.44053 | a |
| 10 | Creole | T4 | 26.66667 | 13.02529 | 26 | -0.1071956 | 53.44053 | b |
| 11 | Hybrid | T5 | 70.00000 | 13.02529 | 26 | 43.2261377 | 96.77386 | a |
| 12 | Creole | T5 | 70.00000 | 13.02529 | 26 | 43.2261377 | 96.77386 | a |
| 13 | Hybrid | T6 | 43.33333 | 13.02529 | 26 | 16.5594710 | 70.10720 | a |
| 14 | Creole | T6 | 33.33333 | 13.02529 | 26 | 6.5594710 | 60.10720 | a |
p1b <- mc %>%
plot_smr(type = "bar"
, x = "trat"
, y = "emmean"
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Germination ('%')"
, xlab = "Treatments"
, glab = "Variety"
, ylimits = c(0, 120, 20)
)
p1b6.1.3 Velocidad de germinación
trait <- "vg"
fb <- germination
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers %>% kable()| index | bloque | trat | variedad | vg | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 7 | 7 | 1 | T2 | Creole | 1 | -1.666667 | -3.372454 | 0.0007450 | 0.0007450159 | 0.0305456 | OUTLIER |
| 34 | 34 | 1 | T4 | Hybrid | 5 | 1.888889 | 3.822114 | 0.0001323 | 0.0001323123 | 0.0055571 | OUTLIER |
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: vg
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 1.3321 0.66607 1.3664 0.274150
## trat 6 8.6594 1.44323 2.9608 0.026214 *
## variedad 1 2.0003 2.00025 4.1035 0.054051 .
## trat:variedad 6 11.5622 1.92703 3.9533 0.006872 **
## Residuals 24 11.6989 0.48745
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ variedad|trat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| variedad | trat | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | Creole | T0 | 2.888889 | 0.4030931 | 24 | 2.0569455 | 3.720832 | a |
| 1 | Hybrid | T0 | 2.333333 | 0.4030931 | 24 | 1.5013900 | 3.165277 | a |
| 3 | Hybrid | T1 | 3.277778 | 0.4030931 | 24 | 2.4458344 | 4.109721 | a |
| 4 | Creole | T1 | 1.500000 | 0.4030931 | 24 | 0.6680567 | 2.331943 | b |
| 6 | Creole | T2 | 3.379630 | 0.5004960 | 24 | 2.3466566 | 4.412603 | a |
| 5 | Hybrid | T2 | 2.833333 | 0.4030931 | 24 | 2.0013900 | 3.665277 | a |
| 7 | Hybrid | T3 | 3.833333 | 0.4030931 | 24 | 3.0013900 | 4.665277 | a |
| 8 | Creole | T3 | 1.666667 | 0.4030931 | 24 | 0.8347233 | 2.498610 | b |
| 9 | Hybrid | T4 | 2.046296 | 0.5004960 | 24 | 1.0133232 | 3.079269 | a |
| 10 | Creole | T4 | 1.333333 | 0.4030931 | 24 | 0.5013900 | 2.165277 | a |
| 12 | Creole | T5 | 3.055556 | 0.4030931 | 24 | 2.2236122 | 3.887499 | a |
| 11 | Hybrid | T5 | 3.000000 | 0.4030931 | 24 | 2.1680567 | 3.831943 | a |
| 14 | Creole | T6 | 2.333333 | 0.4030931 | 24 | 1.5013900 | 3.165277 | a |
| 13 | Hybrid | T6 | 1.833333 | 0.4030931 | 24 | 1.0013900 | 2.665277 | a |
p1c <- mc %>%
plot_smr(type = "bar"
, x = "trat"
, y = "emmean"
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Germination speed (days)"
, xlab = "Treatments"
, glab = "Variety"
, ylimits = c(0, 6, 1)
)
p1c6.1.4 Indice de germinación
trait <- "ig"
fb <- germination
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
rmout$diagplot
rmout$outliers %>% kable()| index | bloque | trat | variedad | ig | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 25 | 25 | 1 | T1 | Hybrid | 0.2 | -1.466667 | -3.29751 | 0.0009755 | 0.0009754607 | 0.0409693 | OUTLIER |
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model)
## Analysis of Variance Table
##
## Response: ig
## Df Sum Sq Mean Sq F value Pr(>F)
## bloque 2 0.3050 0.1525 0.4149 0.664896
## trat 6 10.3540 1.7257 4.6949 0.002507 **
## variedad 1 3.8850 3.8850 10.5697 0.003278 **
## trat:variedad 6 6.5489 1.0915 2.9695 0.024965 *
## Residuals 25 9.1890 0.3676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
mc <- emmeans(model, ~ variedad|trat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group")
mc %>% kable()| variedad | trat | emmean | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | Creole | T0 | 2.6000000 | 0.3500293 | 25 | 1.8791012 | 3.3208988 | a |
| 1 | Hybrid | T0 | 1.7666667 | 0.3500293 | 25 | 1.0457678 | 2.4875655 | a |
| 3 | Hybrid | T1 | 2.3679487 | 0.4341579 | 25 | 1.4737838 | 3.2621137 | a |
| 4 | Creole | T1 | 0.6000000 | 0.3500293 | 25 | -0.1208988 | 1.3208988 | b |
| 5 | Hybrid | T2 | 1.1333333 | 0.3500293 | 25 | 0.4124345 | 1.8542322 | a |
| 6 | Creole | T2 | 0.5666667 | 0.3500293 | 25 | -0.1542322 | 1.2875655 | a |
| 7 | Hybrid | T3 | 1.2333333 | 0.3500293 | 25 | 0.5124345 | 1.9542322 | a |
| 8 | Creole | T3 | 0.1666667 | 0.3500293 | 25 | -0.5542322 | 0.8875655 | b |
| 9 | Hybrid | T4 | 1.9666667 | 0.3500293 | 25 | 1.2457678 | 2.6875655 | a |
| 10 | Creole | T4 | 0.5333333 | 0.3500293 | 25 | -0.1875655 | 1.2542322 | b |
| 11 | Hybrid | T5 | 1.7000000 | 0.3500293 | 25 | 0.9791012 | 2.4208988 | a |
| 12 | Creole | T5 | 1.6666667 | 0.3500293 | 25 | 0.9457678 | 2.3875655 | a |
| 13 | Hybrid | T6 | 1.0666667 | 0.3500293 | 25 | 0.3457678 | 1.7875655 | a |
| 14 | Creole | T6 | 0.5333333 | 0.3500293 | 25 | -0.1875655 | 1.2542322 | a |
p1d <- mc %>%
plot_smr(type = "bar"
, x = "trat"
, y = "emmean"
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, ylab = "Germination Index"
, xlab = "Treatments"
, glab = "Variety"
, ylimits = c(0, 5, 1)
)
p1d6.2 Figura 1
legend <- cowplot::get_plot_component(p1b, 'guide-box-top', return_all = TRUE)
p1i <- list(p1b + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p1c + labs(x = NULL) + theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, p1d + labs(x = NULL) + theme(legend.position="none")
) %>%
plot_grid(plotlist = ., ncol = 1
, labels = c("b", "c", "d")
)
p1il <- list(legend, p1i) %>%
plot_grid(plotlist = ., ncol = 1, align = 'v', rel_heights = c(0.05, 1))
plot <- list(p1a, p1il) %>%
plot_grid(plotlist = .
, ncol = 1
, labels = c("a")
, rel_heights = c(0.6, 1)
)
plot %>%
ggsave2(plot = ., "files/Fig-1.jpg"
, units = "cm"
, width = 24
, height = 29
)
plot %>%
ggsave2(plot = ., "files/Fig-1.eps"
, units = "cm"
, width = 24
, height = 29
)
knitr::include_graphics("files/Fig-1.jpg")6.3 Objetivo Específico 2
Identificar el mejor tratamiento que influye positivamente en el crecimiento y desarrollo de plantulas en el cultivo de Maíz morado.
fb <- plantula %>%
select(!contains("fres"))
rsl <- 5:length(fb) %>% map(\(x) {
trait <- names(fb)[x]
cat("\n### ", trait)
lmm <- paste({{trait}}, "~ 1 + (1|bloque) + trat*variedad") %>% as.formula()
lmd <- paste({{trait}}, "~ bloque + trat*variedad") %>% as.formula()
rmout <- fb %>%
remove_outliers(formula = lmm
, drop_na = T, plot_diag = T)
cat("\n#### ", "Diagnostico")
rmout$diagplot %>% print()
cat("\n#### ", "Outliers")
rmout$outliers %>% kable() %>% print()
cat("\n#### ", "ANOVA")
model <- rmout$data$clean %>%
aov(formula = lmd, .)
anova(model) %>% anova_table %>% kable() %>% print()
cat("\n#### ", "Mean comparison")
mc <- emmeans(model, ~ variedad|trat) %>%
cld(Letters = letters, reversed = T) %>%
mutate(across(.group, trimws)) %>%
rename(group = ".group") %>%
rename({{trait}} := "emmean")
mc %>% kable() %>% print()
plot <- mc %>%
plot_smr(x = "trat"
, y = trait
, group = "variedad"
, sig = "group"
, error = "SE"
, color = T
, xlab = "Treatments"
, glab = "Variety"
)
plot
list(mc = mc, plot = plot)
})6.3.1 raiz_lgtd
6.3.1.1 Diagnostico
6.3.1.2 Outliers
| index | bloque | trat | variedad | raiz_lgtd | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag |
|---|
6.3.1.3 ANOVA
| Factor | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Sig |
|---|---|---|---|---|---|---|
| bloque | 2 | 7.20952380952369 | 3.60476190476185 | 0.419160753734702 | 0.658192586259791 | ns |
| trat | 6 | 113.028571428572 | 18.8380952380953 | 2.19048869455021 | 0.0455341138068884 | * |
| variedad | 1 | 2.30476190476197 | 2.30476190476197 | 0.267997100142146 | 0.605268321276777 | ns |
| trat:variedad | 6 | 240.761904761906 | 40.1269841269843 | 4.66595502175847 | 0.000186149204512116 | *** |
| Residuals | 194 | 1668.39047619048 | 8.59995090819833 | |||
| — | ||||||
| Significance: | 0.001 *** | 0.01 ** | 0.05 * |
6.3.1.4 Mean comparison
| variedad | trat | raiz_lgtd | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 1 | Hybrid | T0 | 16.00000 | 0.7571856 | 194 | 14.506627 | 17.49337 | a |
| 2 | Creole | T0 | 11.00000 | 0.7571856 | 194 | 9.506627 | 12.49337 | b |
| 4 | Creole | T1 | 14.00000 | 0.7571856 | 194 | 12.506627 | 15.49337 | a |
| 3 | Hybrid | T1 | 13.93333 | 0.7571856 | 194 | 12.439961 | 15.42671 | a |
| 5 | Hybrid | T2 | 13.60000 | 0.7571856 | 194 | 12.106627 | 15.09337 | a |
| 6 | Creole | T2 | 13.33333 | 0.7571856 | 194 | 11.839961 | 14.82671 | a |
| 8 | Creole | T3 | 12.93333 | 0.7571856 | 194 | 11.439961 | 14.42671 | a |
| 7 | Hybrid | T3 | 12.46667 | 0.7571856 | 194 | 10.973294 | 13.96004 | a |
| 10 | Creole | T4 | 15.13333 | 0.7571856 | 194 | 13.639961 | 16.62671 | a |
| 9 | Hybrid | T4 | 14.00000 | 0.7571856 | 194 | 12.506627 | 15.49337 | a |
| 12 | Creole | T5 | 15.80000 | 0.7571856 | 194 | 14.306627 | 17.29337 | a |
| 11 | Hybrid | T5 | 13.40000 | 0.7571856 | 194 | 11.906627 | 14.89337 | b |
| 13 | Hybrid | T6 | 15.06667 | 0.7571856 | 194 | 13.573294 | 16.56004 | a |
| 14 | Creole | T6 | 14.80000 | 0.7571856 | 194 | 13.306627 | 16.29337 | a |
6.3.2 gsr_raiz
6.3.2.1 Diagnostico
6.3.2.2 Outliers
| index | bloque | trat | variedad | gsr_raiz | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag |
|---|
6.3.2.3 ANOVA
| Factor | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Sig |
|---|---|---|---|---|---|---|
| bloque | 2 | 0.041745714285714 | 0.020872857142857 | 0.527763692800836 | 0.590767266932123 | ns |
| trat | 6 | 2.85575333333334 | 0.47595888888889 | 12.0344722862891 | 0.0000000000176542819668879 | *** |
| variedad | 1 | 0.327257619047619 | 0.327257619047619 | 8.27460698569895 | 0.00447016897110515 | ** |
| trat:variedad | 6 | 0.701705714285714 | 0.116950952380952 | 2.95706841103296 | 0.00874560396951841 | ** |
| Residuals | 194 | 7.67262761904762 | 0.0395496269023073 | |||
| — | ||||||
| Significance: | 0.001 *** | 0.01 ** | 0.05 * |
6.3.2.4 Mean comparison
| variedad | trat | gsr_raiz | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | Creole | T0 | 1.2560000 | 0.0513482 | 194 | 1.1547275 | 1.3572725 | a |
| 1 | Hybrid | T0 | 1.0853333 | 0.0513482 | 194 | 0.9840609 | 1.1866058 | b |
| 3 | Hybrid | T1 | 0.8926667 | 0.0513482 | 194 | 0.7913942 | 0.9939391 | a |
| 4 | Creole | T1 | 0.7486667 | 0.0513482 | 194 | 0.6473942 | 0.8499391 | b |
| 5 | Hybrid | T2 | 0.9500000 | 0.0513482 | 194 | 0.8487275 | 1.0512725 | a |
| 6 | Creole | T2 | 0.9260000 | 0.0513482 | 194 | 0.8247275 | 1.0272725 | a |
| 8 | Creole | T3 | 0.8600000 | 0.0513482 | 194 | 0.7587275 | 0.9612725 | a |
| 7 | Hybrid | T3 | 0.7760000 | 0.0513482 | 194 | 0.6747275 | 0.8772725 | a |
| 10 | Creole | T4 | 0.9846667 | 0.0513482 | 194 | 0.8833942 | 1.0859391 | a |
| 9 | Hybrid | T4 | 0.7840000 | 0.0513482 | 194 | 0.6827275 | 0.8852725 | b |
| 12 | Creole | T5 | 1.1066667 | 0.0513482 | 194 | 1.0053942 | 1.2079391 | a |
| 11 | Hybrid | T5 | 0.9280000 | 0.0513482 | 194 | 0.8267275 | 1.0292725 | b |
| 14 | Creole | T6 | 1.0513333 | 0.0513482 | 194 | 0.9500609 | 1.1526058 | a |
| 13 | Hybrid | T6 | 0.9646667 | 0.0513482 | 194 | 0.8633942 | 1.0659391 | a |
6.3.3 num_raiz
6.3.3.1 Diagnostico
6.3.3.2 Outliers
| index | bloque | trat | variedad | num_raiz | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag |
|---|
6.3.3.3 ANOVA
| Factor | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Sig |
|---|---|---|---|---|---|---|
| bloque | 2 | 16.8285714285716 | 8.4142857142858 | 1.07887001240016 | 0.342010330536742 | ns |
| trat | 6 | 457.161904761905 | 76.1936507936509 | 9.7694620515435 | 0.0000000021095513439047 | *** |
| variedad | 1 | 4.28571428571414 | 4.28571428571414 | 0.549509344176629 | 0.459414622986879 | ns |
| trat:variedad | 6 | 153.714285714286 | 25.6190476190477 | 3.28484474630041 | 0.00422788227377547 | ** |
| Residuals | 194 | 1513.0380952381 | 7.79916543937164 | |||
| — | ||||||
| Significance: | 0.001 *** | 0.01 ** | 0.05 * |
6.3.3.4 Mean comparison
| variedad | trat | num_raiz | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 1 | Hybrid | T0 | 11.33333 | 0.7210717 | 194 | 9.911187 | 12.75548 | a |
| 2 | Creole | T0 | 10.86667 | 0.7210717 | 194 | 9.444520 | 12.28881 | a |
| 3 | Hybrid | T1 | 15.73333 | 0.7210717 | 194 | 14.311187 | 17.15548 | a |
| 4 | Creole | T1 | 14.53333 | 0.7210717 | 194 | 13.111187 | 15.95548 | a |
| 6 | Creole | T2 | 15.06667 | 0.7210717 | 194 | 13.644520 | 16.48881 | a |
| 5 | Hybrid | T2 | 12.00000 | 0.7210717 | 194 | 10.577854 | 13.42215 | b |
| 7 | Hybrid | T3 | 12.53333 | 0.7210717 | 194 | 11.111187 | 13.95548 | a |
| 8 | Creole | T3 | 10.66667 | 0.7210717 | 194 | 9.244520 | 12.08881 | a |
| 10 | Creole | T4 | 13.40000 | 0.7210717 | 194 | 11.977854 | 14.82215 | a |
| 9 | Hybrid | T4 | 10.93333 | 0.7210717 | 194 | 9.511187 | 12.35548 | b |
| 11 | Hybrid | T5 | 11.80000 | 0.7210717 | 194 | 10.377854 | 13.22215 | a |
| 12 | Creole | T5 | 11.33333 | 0.7210717 | 194 | 9.911187 | 12.75548 | a |
| 14 | Creole | T6 | 10.73333 | 0.7210717 | 194 | 9.311187 | 12.15548 | a |
| 13 | Hybrid | T6 | 10.26667 | 0.7210717 | 194 | 8.844520 | 11.68881 | a |
6.3.4 peso_seco_raiz
6.3.4.1 Diagnostico
6.3.4.2 Outliers
| index | bloque | trat | variedad | peso_seco_raiz | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 124 | 124 | 1 | T1 | Hybrid | 2.60 | 1.300138 | 4.664528 | 0.0000031 | 0.00000309326204 | 0.0006465 | OUTLIER |
| 153 | 153 | 1 | T3 | Hybrid | 2.88 | 1.497472 | 5.372504 | 0.0000001 | 0.00000007765065 | 0.0000163 | OUTLIER |
| 193 | 193 | 3 | T5 | Hybrid | 1.94 | 1.218563 | 4.371860 | 0.0000123 | 0.00001231926601 | 0.0025624 | OUTLIER |
6.3.4.3 ANOVA
| Factor | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Sig |
|---|---|---|---|---|---|---|
| bloque | 2 | 0.708888244834166 | 0.354444122417083 | 3.68543486170346 | 0.0268873108796916 | * |
| trat | 6 | 8.94085063640856 | 1.49014177273476 | 15.4941783225705 | 0.0000000000000187491544735756 | *** |
| variedad | 1 | 0.203977311319524 | 0.203977311319524 | 2.12091285082425 | 0.146941784139003 | ns |
| trat:variedad | 6 | 0.268201941388118 | 0.0447003235646863 | 0.46478449034871 | 0.833811880880163 | ns |
| Residuals | 191 | 18.3692915278854 | 0.0961743011931172 | |||
| — | ||||||
| Significance: | 0.001 *** | 0.01 ** | 0.05 * |
6.3.4.4 Mean comparison
| variedad | trat | peso_seco_raiz | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 1 | Hybrid | T0 | 0.6646667 | 0.0800726 | 191 | 0.5067265 | 0.8226068 | a |
| 2 | Creole | T0 | 0.6346667 | 0.0800726 | 191 | 0.4767265 | 0.7926068 | a |
| 4 | Creole | T1 | 1.2140000 | 0.0800726 | 191 | 1.0560599 | 1.3719401 | a |
| 3 | Hybrid | T1 | 1.1876781 | 0.0829119 | 191 | 1.0241376 | 1.3512186 | a |
| 5 | Hybrid | T2 | 0.8340000 | 0.0800726 | 191 | 0.6760599 | 0.9919401 | a |
| 6 | Creole | T2 | 0.8133333 | 0.0800726 | 191 | 0.6553932 | 0.9712735 | a |
| 7 | Hybrid | T3 | 1.2562495 | 0.0829119 | 191 | 1.0927090 | 1.4197900 | a |
| 8 | Creole | T3 | 1.0566667 | 0.0800726 | 191 | 0.8987265 | 1.2146068 | a |
| 9 | Hybrid | T4 | 0.9240000 | 0.0800726 | 191 | 0.7660599 | 1.0819401 | a |
| 10 | Creole | T4 | 0.8433333 | 0.0800726 | 191 | 0.6853932 | 1.0012735 | a |
| 11 | Hybrid | T5 | 0.6809488 | 0.0829116 | 191 | 0.5174088 | 0.8444889 | a |
| 12 | Creole | T5 | 0.5526667 | 0.0800726 | 191 | 0.3947265 | 0.7106068 | a |
| 13 | Hybrid | T6 | 0.9426667 | 0.0800726 | 191 | 0.7847265 | 1.1006068 | a |
| 14 | Creole | T6 | 0.9320000 | 0.0800726 | 191 | 0.7740599 | 1.0899401 | a |
6.3.5 alt_planta
6.3.5.1 Diagnostico
6.3.5.2 Outliers
| index | bloque | trat | variedad | alt_planta | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag |
|---|
6.3.5.3 ANOVA
| Factor | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Sig |
|---|---|---|---|---|---|---|
| bloque | 2 | 135.895238095238 | 67.947619047619 | 2.57285007649253 | 0.0789200270213045 | ns |
| trat | 6 | 8203.78095238094 | 1367.29682539682 | 51.7729655743322 | 0.0000000000000000000000000000000000000994591853425956 | *** |
| variedad | 1 | 20.742857142857 | 20.742857142857 | 0.785432401233541 | 0.376582109302841 | ns |
| trat:variedad | 6 | 1204.52380952381 | 200.753968253968 | 7.60158883884396 | 0.000000244031006032972 | *** |
| Residuals | 194 | 5123.43809523809 | 26.4094747177221 | |||
| — | ||||||
| Significance: | 0.001 *** | 0.01 ** | 0.05 * |
6.3.5.4 Mean comparison
| variedad | trat | alt_planta | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | Creole | T0 | 30.80000 | 1.326888 | 194 | 28.18302 | 33.41698 | a |
| 1 | Hybrid | T0 | 26.13333 | 1.326888 | 194 | 23.51636 | 28.75031 | b |
| 3 | Hybrid | T1 | 46.33333 | 1.326888 | 194 | 43.71636 | 48.95031 | a |
| 4 | Creole | T1 | 40.26667 | 1.326888 | 194 | 37.64969 | 42.88364 | b |
| 5 | Hybrid | T2 | 44.20000 | 1.326888 | 194 | 41.58302 | 46.81698 | a |
| 6 | Creole | T2 | 39.40000 | 1.326888 | 194 | 36.78302 | 42.01698 | b |
| 8 | Creole | T3 | 38.26667 | 1.326888 | 194 | 35.64969 | 40.88364 | a |
| 7 | Hybrid | T3 | 37.06667 | 1.326888 | 194 | 34.44969 | 39.68364 | a |
| 10 | Creole | T4 | 40.53333 | 1.326888 | 194 | 37.91636 | 43.15031 | a |
| 9 | Hybrid | T4 | 33.06667 | 1.326888 | 194 | 30.44969 | 35.68364 | b |
| 11 | Hybrid | T5 | 30.26667 | 1.326888 | 194 | 27.64969 | 32.88364 | a |
| 12 | Creole | T5 | 27.53333 | 1.326888 | 194 | 24.91636 | 30.15031 | a |
| 13 | Hybrid | T6 | 28.80000 | 1.326888 | 194 | 26.18302 | 31.41698 | a |
| 14 | Creole | T6 | 24.66667 | 1.326888 | 194 | 22.04969 | 27.28364 | b |
6.3.6 gsr_tallo
6.3.6.1 Diagnostico
6.3.6.2 Outliers
| index | bloque | trat | variedad | gsr_tallo | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 139 | 139 | 1 | T2 | Hybrid | 3.04 | -2.328344 | -4.180425 | 0.0000291 | 0.0000290965 | 0.0061103 | OUTLIER |
6.3.6.3 ANOVA
| Factor | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Sig |
|---|---|---|---|---|---|---|
| bloque | 2 | 5.39985048550228 | 2.69992524275114 | 7.27885665755039 | 0.000896145755015216 | *** |
| trat | 6 | 36.0290245706251 | 6.00483742843752 | 16.1887263400496 | 0.00000000000000461731449963955 | *** |
| variedad | 1 | 1.79298499200426 | 1.79298499200426 | 4.8337933729749 | 0.0290959732837517 | * |
| trat:variedad | 6 | 6.51630589531172 | 1.08605098255195 | 2.92793641083642 | 0.00933526126495123 | ** |
| Residuals | 193 | 71.5889316642121 | 0.370927107068456 | |||
| — | ||||||
| Significance: | 0.001 *** | 0.01 ** | 0.05 * |
6.3.6.4 Mean comparison
| variedad | trat | gsr_tallo | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | Creole | T0 | 4.471333 | 0.1572529 | 193 | 4.161179 | 4.781488 | a |
| 1 | Hybrid | T0 | 3.890667 | 0.1572529 | 193 | 3.580512 | 4.200821 | b |
| 3 | Hybrid | T1 | 4.917333 | 0.1572529 | 193 | 4.607178 | 5.227488 | a |
| 4 | Creole | T1 | 4.849333 | 0.1572529 | 193 | 4.539179 | 5.159488 | a |
| 5 | Hybrid | T2 | 5.378330 | 0.1628281 | 193 | 5.057179 | 5.699481 | a |
| 6 | Creole | T2 | 5.032000 | 0.1572529 | 193 | 4.721845 | 5.342155 | a |
| 8 | Creole | T3 | 4.509333 | 0.1572529 | 193 | 4.199179 | 4.819488 | a |
| 7 | Hybrid | T3 | 4.016000 | 0.1572529 | 193 | 3.705845 | 4.326155 | b |
| 10 | Creole | T4 | 4.316667 | 0.1572529 | 193 | 4.006512 | 4.626822 | a |
| 9 | Hybrid | T4 | 3.704000 | 0.1572529 | 193 | 3.393845 | 4.014155 | b |
| 12 | Creole | T5 | 4.195333 | 0.1572529 | 193 | 3.885178 | 4.505488 | a |
| 11 | Hybrid | T5 | 4.066667 | 0.1572529 | 193 | 3.756512 | 4.376822 | a |
| 13 | Hybrid | T6 | 4.218000 | 0.1572529 | 193 | 3.907845 | 4.528155 | a |
| 14 | Creole | T6 | 4.095333 | 0.1572529 | 193 | 3.785178 | 4.405488 | a |
6.3.7 nhp_hoja
6.3.7.1 Diagnostico
6.3.7.2 Outliers
| index | bloque | trat | variedad | nhp_hoja | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 4 | 4 | 1 | T0 | Creole | 6 | 0.9558691 | 4.835436 | 0.0000013 | 0.0000013285389 | 0.0002763 | OUTLIER |
| 5 | 5 | 1 | T0 | Creole | 4 | -1.0441309 | -5.281925 | 0.0000001 | 0.0000001278335 | 0.0000268 | OUTLIER |
| 51 | 51 | 2 | T3 | Creole | 6 | 0.8721830 | 4.412096 | 0.0000102 | 0.0000102374945 | 0.0020987 | OUTLIER |
| 57 | 57 | 3 | T3 | Creole | 6 | 0.9052812 | 4.579529 | 0.0000047 | 0.0000046602472 | 0.0009647 | OUTLIER |
| 66 | 66 | 2 | T4 | Creole | 6 | 0.7388497 | 3.737605 | 0.0001858 | 0.0001857817174 | 0.0369706 | OUTLIER |
| 72 | 72 | 3 | T4 | Creole | 6 | 0.7719479 | 3.905038 | 0.0000942 | 0.0000942105812 | 0.0188421 | OUTLIER |
| 88 | 88 | 3 | T5 | Creole | 6 | 0.9719479 | 4.916774 | 0.0000009 | 0.0000008798196 | 0.0001839 | OUTLIER |
| 152 | 152 | 1 | T3 | Hybrid | 6 | 0.8225357 | 4.160946 | 0.0000317 | 0.0000316932621 | 0.0064337 | OUTLIER |
| 155 | 155 | 1 | T3 | Hybrid | 6 | 0.8225357 | 4.160946 | 0.0000317 | 0.0000316932621 | 0.0064337 | OUTLIER |
| 166 | 166 | 1 | T4 | Hybrid | 6 | 0.8225357 | 4.160946 | 0.0000317 | 0.0000316932621 | 0.0064337 | OUTLIER |
| 173 | 173 | 2 | T4 | Hybrid | 6 | 0.8721830 | 4.412096 | 0.0000102 | 0.0000102374945 | 0.0020987 | OUTLIER |
| 181 | 181 | 1 | T5 | Hybrid | 6 | 0.8892024 | 4.498191 | 0.0000069 | 0.0000068534130 | 0.0014118 | OUTLIER |
6.3.7.3 ANOVA
| Factor | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Sig |
|---|---|---|---|---|---|---|
| bloque | 2 | 0.684993592642831 | 0.342496796321415 | 3.22074419272831 | 0.0422116625843577 | * |
| trat | 6 | 13.0076855867699 | 2.16794759779498 | 20.3867735719924 | 0.00000000000000000344316718685895 | *** |
| variedad | 1 | 0.271082180181899 | 0.271082180181899 | 2.54918109293389 | 0.112086289536158 | ns |
| trat:variedad | 6 | 0.722601130029324 | 0.120433521671554 | 1.13252319350008 | 0.345154455799285 | ns |
| Residuals | 182 | 19.3540415507802 | 0.106340887641649 | |||
| — | ||||||
| Significance: | 0.001 *** | 0.01 ** | 0.05 * |
6.3.7.4 Mean comparison
| variedad | trat | nhp_hoja | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | Creole | T0 | 5.010904 | 0.0905876 | 182 | 4.832167 | 5.189641 | a |
| 1 | Hybrid | T0 | 4.666667 | 0.0841985 | 182 | 4.500536 | 4.832797 | b |
| 4 | Creole | T1 | 5.533333 | 0.0841985 | 182 | 5.367203 | 5.699464 | a |
| 3 | Hybrid | T1 | 5.466667 | 0.0841985 | 182 | 5.300536 | 5.632797 | a |
| 5 | Hybrid | T2 | 5.533333 | 0.0841985 | 182 | 5.367203 | 5.699464 | a |
| 6 | Creole | T2 | 5.533333 | 0.0841985 | 182 | 5.367203 | 5.699464 | a |
| 7 | Hybrid | T3 | 5.010904 | 0.0905876 | 182 | 4.832167 | 5.189641 | a |
| 8 | Creole | T3 | 4.994548 | 0.0904797 | 182 | 4.816024 | 5.173072 | a |
| 10 | Creole | T4 | 5.148394 | 0.0904797 | 182 | 4.969870 | 5.326918 | a |
| 9 | Hybrid | T4 | 5.005028 | 0.0904787 | 182 | 4.826506 | 5.183550 | a |
| 11 | Hybrid | T5 | 5.005063 | 0.0871860 | 182 | 4.833037 | 5.177088 | a |
| 12 | Creole | T5 | 4.995331 | 0.0871851 | 182 | 4.823308 | 5.167355 | a |
| 13 | Hybrid | T6 | 5.000000 | 0.0841985 | 182 | 4.833869 | 5.166131 | a |
| 14 | Creole | T6 | 5.000000 | 0.0841985 | 182 | 4.833869 | 5.166131 | a |
6.3.8 larg_hoja
6.3.8.1 Diagnostico
6.3.8.2 Outliers
| index | bloque | trat | variedad | larg_hoja | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag |
|---|
6.3.8.3 ANOVA
| Factor | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Sig |
|---|---|---|---|---|---|---|
| bloque | 2 | 24.2666666666667 | 12.1333333333333 | 0.91282316442606 | 0.403106357049745 | ns |
| trat | 6 | 3517.25714285715 | 586.209523809525 | 44.1021125720196 | 0.000000000000000000000000000000000935746697641098 | *** |
| variedad | 1 | 12.8761904761905 | 12.8761904761905 | 0.968710296941942 | 0.326227923148263 | ns |
| trat:variedad | 6 | 830.857142857142 | 138.47619047619 | 10.4179347023194 | 0.000000000526043977707136 | *** |
| Residuals | 194 | 2578.66666666667 | 13.2920962199313 | |||
| — | ||||||
| Significance: | 0.001 *** | 0.01 ** | 0.05 * |
6.3.8.4 Mean comparison
| variedad | trat | larg_hoja | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | Creole | T0 | 26.20000 | 0.94135 | 194 | 24.34341 | 28.05659 | a |
| 1 | Hybrid | T0 | 22.66667 | 0.94135 | 194 | 20.81007 | 24.52326 | b |
| 3 | Hybrid | T1 | 36.26667 | 0.94135 | 194 | 34.41007 | 38.12326 | a |
| 4 | Creole | T1 | 28.33333 | 0.94135 | 194 | 26.47674 | 30.18993 | b |
| 6 | Creole | T2 | 34.80000 | 0.94135 | 194 | 32.94341 | 36.65659 | a |
| 5 | Hybrid | T2 | 33.86667 | 0.94135 | 194 | 32.01007 | 35.72326 | a |
| 7 | Hybrid | T3 | 28.93333 | 0.94135 | 194 | 27.07674 | 30.78993 | a |
| 8 | Creole | T3 | 28.60000 | 0.94135 | 194 | 26.74341 | 30.45659 | a |
| 10 | Creole | T4 | 28.26667 | 0.94135 | 194 | 26.41007 | 30.12326 | a |
| 9 | Hybrid | T4 | 23.33333 | 0.94135 | 194 | 21.47674 | 25.18993 | b |
| 11 | Hybrid | T5 | 26.46667 | 0.94135 | 194 | 24.61007 | 28.32326 | a |
| 12 | Creole | T5 | 23.40000 | 0.94135 | 194 | 21.54341 | 25.25659 | b |
| 13 | Hybrid | T6 | 23.06667 | 0.94135 | 194 | 21.21007 | 24.92326 | a |
| 14 | Creole | T6 | 21.53333 | 0.94135 | 194 | 19.67674 | 23.38993 | a |
6.3.9 grs_hoja
6.3.9.1 Diagnostico
6.3.9.2 Outliers
| index | bloque | trat | variedad | grs_hoja | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 193 | 193 | 3 | T5 | Hybrid | 1.43 | 0.5793333 | 3.757259 | 0.0001718 | 0.0001717844 | 0.0359029 | OUTLIER |
| 197 | 197 | 1 | T6 | Hybrid | 1.45 | 0.6780000 | 4.397161 | 0.0000110 | 0.0000109676 | 0.0023032 | OUTLIER |
6.3.9.3 ANOVA
| Factor | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Sig |
|---|---|---|---|---|---|---|
| bloque | 2 | 0.0676350949593889 | 0.0338175474796944 | 1.18553751980215 | 0.307808659010028 | ns |
| trat | 6 | 1.87309077321147 | 0.312181795535245 | 10.9441180448838 | 0.00000000017837157586309 | *** |
| variedad | 1 | 0.000660363330975994 | 0.000660363330975994 | 0.0231502744556993 | 0.879226703591861 | ns |
| trat:variedad | 6 | 0.634360294681688 | 0.105726715780281 | 3.70644821237387 | 0.00164817394159611 | ** |
| Residuals | 192 | 5.47681453150879 | 0.0285250756849416 | |||
| — | ||||||
| Significance: | 0.001 *** | 0.01 ** | 0.05 * |
6.3.9.4 Mean comparison
| variedad | trat | grs_hoja | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | Creole | T0 | 0.9700000 | 0.0436082 | 192 | 0.8839874 | 1.0560126 | a |
| 1 | Hybrid | T0 | 0.8846667 | 0.0436082 | 192 | 0.7986541 | 0.9706793 | a |
| 3 | Hybrid | T1 | 0.8446667 | 0.0436082 | 192 | 0.7586541 | 0.9306793 | a |
| 4 | Creole | T1 | 0.6346667 | 0.0436082 | 192 | 0.5486541 | 0.7206793 | b |
| 5 | Hybrid | T2 | 0.7553333 | 0.0436082 | 192 | 0.6693207 | 0.8413459 | a |
| 6 | Creole | T2 | 0.7006667 | 0.0436082 | 192 | 0.6146541 | 0.7866793 | a |
| 7 | Hybrid | T3 | 0.6880000 | 0.0436082 | 192 | 0.6019874 | 0.7740126 | a |
| 8 | Creole | T3 | 0.6300000 | 0.0436082 | 192 | 0.5439874 | 0.7160126 | a |
| 10 | Creole | T4 | 0.6853333 | 0.0436082 | 192 | 0.5993207 | 0.7713459 | a |
| 9 | Hybrid | T4 | 0.6366667 | 0.0436082 | 192 | 0.5506541 | 0.7226793 | a |
| 12 | Creole | T5 | 0.9406667 | 0.0436082 | 192 | 0.8546541 | 1.0266793 | a |
| 11 | Hybrid | T5 | 0.8075459 | 0.0451543 | 192 | 0.7184838 | 0.8966081 | b |
| 14 | Creole | T6 | 0.8126667 | 0.0436082 | 192 | 0.7266541 | 0.8986793 | a |
| 13 | Hybrid | T6 | 0.7247584 | 0.0451543 | 192 | 0.6356962 | 0.8138205 | a |
6.3.10 anch_hoja
6.3.10.1 Diagnostico
6.3.10.2 Outliers
| index | bloque | trat | variedad | anch_hoja | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag |
|---|
6.3.10.3 ANOVA
| Factor | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Sig |
|---|---|---|---|---|---|---|
| bloque | 2 | 15.7079895238095 | 7.85399476190473 | 1.13598726704289 | 0.323231382586704 | ns |
| trat | 6 | 291.914404761905 | 48.6524007936508 | 7.0369932102235 | 0.000000862224352741745 | *** |
| variedad | 1 | 0.350554285714286 | 0.350554285714286 | 0.0507035231179815 | 0.822080596652288 | ns |
| trat:variedad | 6 | 139.877105714286 | 23.3128509523809 | 3.3719276168642 | 0.00348053301722631 | ** |
| Residuals | 194 | 1341.27822380952 | 6.91380527736868 | |||
| — | ||||||
| Significance: | 0.001 *** | 0.01 ** | 0.05 * |
6.3.10.4 Mean comparison
| variedad | trat | anch_hoja | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | Creole | T0 | 18.86800 | 0.6789112 | 194 | 17.52901 | 20.20699 | a |
| 1 | Hybrid | T0 | 16.61133 | 0.6789112 | 194 | 15.27234 | 17.95033 | b |
| 3 | Hybrid | T1 | 20.04333 | 0.6789112 | 194 | 18.70434 | 21.38233 | a |
| 4 | Creole | T1 | 18.95867 | 0.6789112 | 194 | 17.61967 | 20.29766 | a |
| 5 | Hybrid | T2 | 21.76933 | 0.6789112 | 194 | 20.43034 | 23.10833 | a |
| 6 | Creole | T2 | 19.03467 | 0.6789112 | 194 | 17.69567 | 20.37366 | b |
| 8 | Creole | T3 | 19.05067 | 0.6789112 | 194 | 17.71167 | 20.38966 | a |
| 7 | Hybrid | T3 | 17.51800 | 0.6789112 | 194 | 16.17901 | 18.85699 | a |
| 10 | Creole | T4 | 17.06933 | 0.6789112 | 194 | 15.73034 | 18.40833 | a |
| 9 | Hybrid | T4 | 16.07400 | 0.6789112 | 194 | 14.73501 | 17.41299 | a |
| 11 | Hybrid | T5 | 19.51800 | 0.6789112 | 194 | 18.17901 | 20.85699 | a |
| 12 | Creole | T5 | 18.28667 | 0.6789112 | 194 | 16.94767 | 19.62566 | a |
| 13 | Hybrid | T6 | 17.86067 | 0.6789112 | 194 | 16.52167 | 19.19966 | a |
| 14 | Creole | T6 | 17.55467 | 0.6789112 | 194 | 16.21567 | 18.89366 | a |
6.3.11 peso_seco_brote
6.3.11.1 Diagnostico
6.3.11.2 Outliers
| index | bloque | trat | variedad | peso_seco_brote | resi | res_MAD | rawp.BHStud | adjp | bholm | out_flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 42 | 42 | 3 | T2 | Creole | 2.39 | 1.216944 | 3.686305 | 0.0002275 | 0.00022753340922 | 0.0464168 | OUTLIER |
| 72 | 72 | 3 | T4 | Creole | 3.09 | 1.776277 | 5.380610 | 0.0000001 | 0.00000007423391 | 0.0000156 | OUTLIER |
| 127 | 127 | 2 | T1 | Hybrid | 3.27 | 1.233120 | 3.735306 | 0.0001875 | 0.00018748728839 | 0.0384349 | OUTLIER |
| 134 | 134 | 3 | T1 | Hybrid | 0.73 | -1.285056 | -3.892628 | 0.0000992 | 0.00009916401076 | 0.0204278 | OUTLIER |
| 169 | 169 | 1 | T4 | Hybrid | 2.85 | 1.391936 | 4.216384 | 0.0000248 | 0.00002482511247 | 0.0051388 | OUTLIER |
| 170 | 170 | 1 | T4 | Hybrid | 2.93 | 1.471936 | 4.458716 | 0.0000082 | 0.00000824521585 | 0.0017150 | OUTLIER |
| 172 | 172 | 2 | T4 | Hybrid | 2.98 | 1.503120 | 4.553176 | 0.0000053 | 0.00000528419693 | 0.0011044 | OUTLIER |
6.3.11.3 ANOVA
| Factor | Df | Sum Sq | Mean Sq | F value | Pr(>F) | Sig |
|---|---|---|---|---|---|---|
| bloque | 2 | 0.502411537322084 | 0.251205768661042 | 1.46529227890307 | 0.233650440367885 | ns |
| trat | 6 | 43.010518479078 | 7.168419746513 | 41.8136500705716 | 0.0000000000000000000000000000000424415542003033 | *** |
| variedad | 1 | 0.0341827573920979 | 0.0341827573920979 | 0.199389252664187 | 0.655730939528593 | ns |
| trat:variedad | 6 | 3.43432182335683 | 0.572386970559472 | 3.33875377534505 | 0.00378990456664722 | ** |
| Residuals | 187 | 32.0587772255111 | 0.17143731136637 | |||
| — | ||||||
| Significance: | 0.001 *** | 0.01 ** | 0.05 * |
6.3.11.4 Mean comparison
| variedad | trat | peso_seco_brote | SE | df | lower.CL | upper.CL | group | |
|---|---|---|---|---|---|---|---|---|
| 2 | Creole | T0 | 0.7100000 | 0.1069072 | 187 | 0.4991008 | 0.9208992 | a |
| 1 | Hybrid | T0 | 0.4253333 | 0.1069072 | 187 | 0.2144341 | 0.6362325 | a |
| 3 | Hybrid | T1 | 2.0300403 | 0.1148803 | 187 | 1.8034124 | 2.2566683 | a |
| 4 | Creole | T1 | 1.5046667 | 0.1069072 | 187 | 1.2937675 | 1.7155659 | b |
| 5 | Hybrid | T2 | 1.2113333 | 0.1069072 | 187 | 1.0004341 | 1.4222325 | a |
| 6 | Creole | T2 | 1.0926154 | 0.1106987 | 187 | 0.8742365 | 1.3109942 | a |
| 8 | Creole | T3 | 1.7186667 | 0.1069072 | 187 | 1.5077675 | 1.9295659 | a |
| 7 | Hybrid | T3 | 1.4093333 | 0.1069072 | 187 | 1.1984341 | 1.6202325 | b |
| 10 | Creole | T4 | 1.1933296 | 0.1106987 | 187 | 0.9749508 | 1.4117085 | a |
| 9 | Hybrid | T4 | 1.0985717 | 0.1196740 | 187 | 0.8624872 | 1.3346563 | a |
| 12 | Creole | T5 | 0.6500000 | 0.1069072 | 187 | 0.4391008 | 0.8608992 | a |
| 11 | Hybrid | T5 | 0.6373333 | 0.1069072 | 187 | 0.4264341 | 0.8482325 | a |
| 14 | Creole | T6 | 0.5486667 | 0.1069072 | 187 | 0.3377675 | 0.7595659 | a |
| 13 | Hybrid | T6 | 0.4586667 | 0.1069072 | 187 | 0.2477675 | 0.6695659 | a |
6.3.12 Figure 2
legend <- cowplot::get_plot_component(rsl[[1]]$plot, 'guide-box-top', return_all = TRUE)
fig <- list(
rsl[[1]]$plot + labs(x = NULL, y = "Root length (cm)") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 20), n.breaks = 5) +
theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, rsl[[2]]$plot + labs(x = NULL, y = "Root thickness (mm)") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 2), n.breaks = 5) +
theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, rsl[[3]]$plot + labs(x = NULL, y = "Root number") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 20), n.breaks = 5) +
theme(legend.position="none"
, axis.title.x=element_blank()
, axis.text.x=element_blank()
, axis.ticks.x=element_blank())
, rsl[[4]]$plot + labs(y = "Root dry weight (g)") +
scale_y_continuous(expand = c(0, 0), limits = c(0, 2), n.breaks = 5) +
theme(legend.position="none")
) %>%
plot_grid(plotlist = .
, ncol = 1
, labels = "auto"
)
plot <- list(legend, fig) %>%
plot_grid(plotlist = .
, ncol = 1
, rel_heights = c(0.05, 1)
)
plot %>%
ggsave2(plot = .
, "files/Fig-2.jpg"
, units = "cm"
, width = 12
, height = 24
)
plot %>%
ggsave2(plot = .
, "files/Fig-2.eps"
, units = "cm"
, width = 12
, height = 24
)
include_graphics("files/Fig-2.jpg")6.3.13 Table
tab <- 5:length(rsl) %>% map(\(x) {
trait <- names(rsl[[x]]$mc)[[3]]
rsl[[x]]$mc %>%
mutate(across(where(is.numeric), ~ round(., 2))) %>%
unite({{trait}}, c({{trait}}, group), sep = " ") %>%
select(1:3)
}) %>%
Reduce(function(...) merge(..., all = TRUE), .) %>%
rename(Variety = "variedad"
, Treatment = trat
, "Plant height (cm)" = "alt_planta"
, "Stem thickness (mm)" = "gsr_tallo"
, "Leaves number" = "nhp_hoja"
, "Leaf length (cm)" = "larg_hoja"
, "Leaf thickness (mm)" = "grs_hoja"
, "Leaf width (mm)" = "anch_hoja"
, "Shoot Dry weight (g)" = "peso_seco_brote"
) %>%
arrange(Treatment)
tab %>% kable()| Variety | Treatment | Plant height (cm) | Stem thickness (mm) | Leaves number | Leaf length (cm) | Leaf thickness (mm) | Leaf width (mm) | Shoot Dry weight (g) |
|---|---|---|---|---|---|---|---|---|
| Creole | T0 | 30.8 a | 4.47 a | 5.01 a | 26.2 a | 0.97 a | 18.87 a | 0.71 a |
| Hybrid | T0 | 26.13 b | 3.89 b | 4.67 b | 22.67 b | 0.88 a | 16.61 b | 0.43 a |
| Creole | T1 | 40.27 b | 4.85 a | 5.53 a | 28.33 b | 0.63 b | 18.96 a | 1.5 b |
| Hybrid | T1 | 46.33 a | 4.92 a | 5.47 a | 36.27 a | 0.84 a | 20.04 a | 2.03 a |
| Creole | T2 | 39.4 b | 5.03 a | 5.53 a | 34.8 a | 0.7 a | 19.03 b | 1.09 a |
| Hybrid | T2 | 44.2 a | 5.38 a | 5.53 a | 33.87 a | 0.76 a | 21.77 a | 1.21 a |
| Creole | T3 | 38.27 a | 4.51 a | 4.99 a | 28.6 a | 0.63 a | 19.05 a | 1.72 a |
| Hybrid | T3 | 37.07 a | 4.02 b | 5.01 a | 28.93 a | 0.69 a | 17.52 a | 1.41 b |
| Creole | T4 | 40.53 a | 4.32 a | 5.15 a | 28.27 a | 0.69 a | 17.07 a | 1.19 a |
| Hybrid | T4 | 33.07 b | 3.7 b | 5.01 a | 23.33 b | 0.64 a | 16.07 a | 1.1 a |
| Creole | T5 | 27.53 a | 4.2 a | 5 a | 23.4 b | 0.94 a | 18.29 a | 0.65 a |
| Hybrid | T5 | 30.27 a | 4.07 a | 5.01 a | 26.47 a | 0.81 b | 19.52 a | 0.64 a |
| Creole | T6 | 24.67 b | 4.1 a | 5 a | 21.53 a | 0.81 a | 17.55 a | 0.55 a |
| Hybrid | T6 | 28.8 a | 4.22 a | 5 a | 23.07 a | 0.72 a | 17.86 a | 0.46 a |
tab %>% sheet_write(data = ., gs, "table")6.3.14 PCA
blues <- 1:length(rsl) %>% map(\(x) {
rsl[[x]]$mc %>%
select(1:3)
}) %>%
Reduce(function(...) merge(..., all = TRUE), .) %>%
rename(Variety = "variedad"
, Treatment = trat
, "Plant height (cm)" = "alt_planta"
, "Stem thickness (mm)" = "gsr_tallo"
, "Leaves number" = "nhp_hoja"
, "Leaf length (cm)" = "larg_hoja"
, "Leaf thickness (mm)" = "grs_hoja"
, "Leaf width (mm)" = "anch_hoja"
, "Shoot Dry weight (g)" = "peso_seco_brote"
#>
, "Root length (cm)" = "raiz_lgtd"
, "Root thickness (mm)" = "gsr_raiz"
, "Root number" = "num_raiz"
, "Root Dry weight (g)" = peso_seco_raiz
)
blues %>% str()
## 'data.frame': 14 obs. of 13 variables:
## $ Variety : Factor w/ 2 levels "Creole","Hybrid": 1 1 1 1 1 1 1 2 2 2 ...
## $ Treatment : Factor w/ 7 levels "T0","T1","T2",..: 1 2 3 4 5 6 7 1 2 3 ...
## $ Root length (cm) : num 11 14 13.3 12.9 15.1 ...
## $ Root thickness (mm) : num 1.256 0.749 0.926 0.86 0.985 ...
## $ Root number : num 10.9 14.5 15.1 10.7 13.4 ...
## $ Root Dry weight (g) : num 0.635 1.214 0.813 1.057 0.843 ...
## $ Plant height (cm) : num 30.8 40.3 39.4 38.3 40.5 ...
## $ Stem thickness (mm) : num 4.47 4.85 5.03 4.51 4.32 ...
## $ Leaves number : num 5.01 5.53 5.53 4.99 5.15 ...
## $ Leaf length (cm) : num 26.2 28.3 34.8 28.6 28.3 ...
## $ Leaf thickness (mm) : num 0.97 0.635 0.701 0.63 0.685 ...
## $ Leaf width (mm) : num 18.9 19 19 19.1 17.1 ...
## $ Shoot Dry weight (g): num 0.71 1.5 1.09 1.72 1.19 ...pca <- blues %>%
select(!c("Leaves number")) %>%
unite("treat", c(Treatment, Variety), remove = F, sep = "-") %>%
column_to_rownames("treat") %>%
PCA(scale.unit = T, quali.sup = c(1:2), graph = F)
summary(pca, nbelements = Inf, nb.dec = 2)
##
## Call:
## PCA(X = ., scale.unit = T, quali.sup = c(1:2), graph = F)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7 Dim.8
## Variance 5.26 2.29 0.98 0.58 0.34 0.24 0.19 0.06
## % of var. 52.62 22.93 9.78 5.75 3.44 2.40 1.90 0.63
## Cumulative % of var. 52.62 75.55 85.33 91.09 94.53 96.93 98.83 99.46
## Dim.9 Dim.10
## Variance 0.03 0.02
## % of var. 0.33 0.21
## Cumulative % of var. 99.79 100.00
##
## Individuals
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3
## T0-Creole | 4.11 | -1.80 4.39 0.19 | 2.83 25.00 0.48 | -2.05
## T1-Creole | 3.21 | 2.73 10.15 0.73 | -1.24 4.77 0.15 | 0.45
## T2-Creole | 2.92 | 2.18 6.45 0.56 | 1.03 3.29 0.12 | 0.62
## T3-Creole | 2.55 | 1.55 3.26 0.37 | -0.93 2.68 0.13 | -1.33
## T4-Creole | 1.93 | 0.33 0.15 0.03 | -0.68 1.44 0.12 | 1.22
## T5-Creole | 3.41 | -2.87 11.15 0.71 | 1.25 4.88 0.13 | 1.14
## T6-Creole | 2.81 | -2.57 9.00 0.84 | -0.45 0.64 0.03 | 0.00
## T0-Hybrid | 3.66 | -3.42 15.88 0.87 | -0.03 0.00 0.00 | 1.23
## T1-Hybrid | 4.47 | 3.94 21.11 0.78 | 0.97 2.95 0.05 | 0.98
## T2-Hybrid | 3.74 | 2.46 8.24 0.44 | 2.23 15.45 0.36 | -0.13
## T3-Hybrid | 2.87 | 1.38 2.60 0.23 | -1.96 11.91 0.47 | -1.16
## T4-Hybrid | 3.07 | -0.95 1.22 0.10 | -2.75 23.47 0.80 | -0.50
## T5-Hybrid | 1.94 | -1.10 1.65 0.32 | 0.60 1.11 0.09 | -0.53
## T6-Hybrid | 2.42 | -1.87 4.76 0.60 | -0.88 2.41 0.13 | 0.06
## ctr cos2
## T0-Creole 30.57 0.25 |
## T1-Creole 1.48 0.02 |
## T2-Creole 2.77 0.04 |
## T3-Creole 12.86 0.27 |
## T4-Creole 10.79 0.40 |
## T5-Creole 9.48 0.11 |
## T6-Creole 0.00 0.00 |
## T0-Hybrid 11.12 0.11 |
## T1-Hybrid 7.05 0.05 |
## T2-Hybrid 0.13 0.00 |
## T3-Hybrid 9.82 0.16 |
## T4-Hybrid 1.83 0.03 |
## T5-Hybrid 2.08 0.08 |
## T6-Hybrid 0.03 0.00 |
##
## Variables
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## Root length (cm) | -0.39 2.82 0.15 | -0.23 2.34 0.05 | 0.84 72.56
## Root thickness (mm) | -0.63 7.63 0.40 | 0.70 21.21 0.49 | 0.01 0.01
## Root number | 0.75 10.80 0.57 | 0.10 0.47 0.01 | 0.46 21.30
## Root Dry weight (g) | 0.68 8.80 0.46 | -0.57 13.96 0.32 | -0.12 1.39
## Plant height (cm) | 0.96 17.35 0.91 | 0.07 0.23 0.01 | 0.08 0.62
## Stem thickness (mm) | 0.74 10.38 0.55 | 0.55 13.37 0.31 | 0.11 1.13
## Leaf length (cm) | 0.90 15.41 0.81 | 0.35 5.27 0.12 | 0.08 0.60
## Leaf thickness (mm) | -0.51 5.00 0.26 | 0.72 22.60 0.52 | 0.08 0.65
## Leaf width (mm) | 0.58 6.43 0.34 | 0.67 19.31 0.44 | -0.12 1.50
## Shoot Dry weight (g) | 0.90 15.38 0.81 | -0.17 1.22 0.03 | -0.05 0.25
## cos2
## Root length (cm) 0.71 |
## Root thickness (mm) 0.00 |
## Root number 0.21 |
## Root Dry weight (g) 0.01 |
## Plant height (cm) 0.01 |
## Stem thickness (mm) 0.01 |
## Leaf length (cm) 0.01 |
## Leaf thickness (mm) 0.01 |
## Leaf width (mm) 0.01 |
## Shoot Dry weight (g) 0.00 |
##
## Supplementary categories
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test Dim.3
## Creole | 0.41 | -0.06 0.02 -0.10 | 0.26 0.41 0.62 | 0.01
## Hybrid | 0.41 | 0.06 0.02 0.10 | -0.26 0.41 -0.62 | -0.01
## T0 | 3.08 | -2.61 0.72 -1.67 | 1.40 0.21 1.36 | -0.41
## T1 | 3.52 | 3.34 0.90 2.14 | -0.13 0.00 -0.13 | 0.72
## T2 | 3.02 | 2.32 0.59 1.49 | 1.63 0.29 1.58 | 0.24
## T3 | 2.44 | 1.47 0.36 0.94 | -1.44 0.35 -1.40 | -1.24
## T4 | 2.02 | -0.31 0.02 -0.20 | -1.71 0.72 -1.66 | 0.36
## T5 | 2.36 | -1.98 0.71 -1.27 | 0.92 0.15 0.90 | 0.30
## T6 | 2.53 | -2.22 0.77 -1.43 | -0.67 0.07 -0.65 | 0.03
## cos2 v.test
## Creole 0.00 0.03 |
## Hybrid 0.00 -0.03 |
## T0 0.02 -0.60 |
## T1 0.04 1.07 |
## T2 0.01 0.36 |
## T3 0.26 -1.85 |
## T4 0.03 0.53 |
## T5 0.02 0.45 |
## T6 0.00 0.05 |
pcainfo <- factoextra::get_pca_var(pca)
pcainfo$cor
## Dim.1 Dim.2 Dim.3 Dim.4
## Root length (cm) -0.3850142 -0.23160568 0.842557174 -0.23147791
## Root thickness (mm) -0.6334207 0.69746926 0.009092441 0.15767946
## Root number 0.7538553 0.10399598 0.456517815 0.36200737
## Root Dry weight (g) 0.6804806 -0.56588136 -0.116435572 0.10078095
## Plant height (cm) 0.9555792 0.07280624 0.077966273 0.01295185
## Stem thickness (mm) 0.7388485 0.55381697 0.105138493 -0.26706820
## Leaf length (cm) 0.9003496 0.34780450 0.076577268 0.07725203
## Leaf thickness (mm) -0.5131467 0.72000262 0.079603893 0.34016346
## Leaf width (mm) 0.5818081 0.66556182 -0.121233647 -0.35752445
## Shoot Dry weight (g) 0.8995921 -0.16715747 -0.049475693 0.18589699
## Dim.5
## Root length (cm) 0.156691674
## Root thickness (mm) 0.022040900
## Root number -0.142026846
## Root Dry weight (g) 0.373757353
## Plant height (cm) -0.081352886
## Stem thickness (mm) 0.005369376
## Leaf length (cm) -0.125316855
## Leaf thickness (mm) 0.276041379
## Leaf width (mm) 0.184068744
## Shoot Dry weight (g) 0.164719886
pcainfo$contrib
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5
## Root length (cm) 2.817341 2.3388953 72.555264317 9.31529745 7.127583908
## Root thickness (mm) 7.625538 21.2110668 0.008449503 4.32242925 0.141029240
## Root number 10.800951 0.4715693 21.300295456 22.78308086 5.855869417
## Root Dry weight (g) 8.800706 13.9624989 1.385611659 1.76577167 40.553653983
## Plant height (cm) 17.354792 0.2311264 0.621274762 0.02916359 1.921305491
## Stem thickness (mm) 10.375209 13.3734939 1.129779427 12.40000834 0.008369473
## Leaf length (cm) 15.406653 5.2745177 0.599335367 1.03752237 4.558998802
## Leaf thickness (mm) 5.004596 22.6037514 0.647647639 20.11652502 22.120700640
## Leaf width (mm) 6.433473 19.3147533 1.502161088 22.22230592 9.835820578
## Shoot Dry weight (g) 15.380741 1.2183272 0.250180782 6.00789552 7.8766684686.3.15 Figure 3
var <- pca %>%
plot.PCA(choix = "var"
, cex = 0.7
)
ind <- pca %>%
plot.PCA(choix = "ind", habillage = 2
, label = c("ind")
, invisible = "quali"
) +
labs(colour = "Treatments") +
theme(legend.position = "bottom"
, legend.direction = "horizontal") +
guides(colour = guide_legend(nrow = 1))
fig <- list(var, ind) %>%
plot_grid(plotlist = .
, ncol = 2
, labels = "auto"
, rel_widths = c(1.5, 2)
)
fig %>%
ggsave2(plot = .
, "files/Fig-3.jpg"
, units = "cm"
, width = 30
, height = 12
)
fig %>%
ggsave2(plot = .
, "files/Fig-3.eps"
, units = "cm"
, width = 30
, height = 12
)
include_graphics("files/Fig-3.jpg")6.3.16 Principal components
var <- get_pca_var(pca)
tmp <- tempfile(fileext = ".png")
ppi <- 300
png(tmp, width=8*ppi, height=12*ppi, res=ppi)
corrplot(var$cor,
method="number",
tl.col="black",
tl.srt=45,)
graphics.off()
pt1 <- png::readPNG(tmp) %>%
grid::rasterGrob(interpolate = TRUE)
pt2 <- fviz_eig(pca,
addlabels=TRUE,
hjust = 0.05,
barfill="white",
barcolor ="darkblue",
linecolor ="red") +
ylim(0, 60) +
labs(
title = "PCA - percentage of explained variances",
y = "Variance (%)") +
theme_minimal()
pt3 <- fviz_contrib(pca,
choice = "var",
axes = 1,
top = 10,
fill="white",
color ="darkblue",
sort.val = "desc") +
ylim(0, 20) +
labs(title = "Dim 1 - variables contribution")
pt4 <- fviz_contrib(pca,
choice = "var",
axes = 2,
top = 10,
fill="white",
color ="darkblue",
sort.val = "desc") +
ylim(0, 25) +
labs(title = "Dim 2 - variables contribution")
plot <- ggdraw(xlim = c(0.0, 1.0), ylim = c(0, 1.0))+
draw_plot(pt1, width = 0.4, height = 0.99, x = 0.62, y = 0.0) +
draw_plot(pt2, width = 0.6, height = 0.34, x = 0.03, y = 0.66) +
draw_plot(pt3, width = 0.6, height = 0.34, x = 0.03, y = 0.33) +
draw_plot(pt4, width = 0.6, height = 0.34, x = 0.03, y = 0.0) +
draw_plot_label(
label = c("a", "b", "c", "d"),
x = c(0.005, 0.005, 0.005, 0.65),
y = c(0.999, 0.67, 0.34, 0.999))
ggsave2(plot = plot, "files/FigS1.jpg", height = 25, width = 46, units = "cm")
ggsave2(plot = plot, "files/FigS1.eps", height = 25, width = 46, units = "cm")
knitr::include_graphics("files/FigS1.jpg")